Types of Variables

A. Categorical Encoding

1. Nominal Variable -
   - Where we do not bother about the arrangement of the categories
   Eg - Gender, City**

   • One Hot Encoding
   • One Hot Encoding with many Categories
   • Mean Encoding

2. Ordinal Variable -

   - Where we worry about the order of the categories, These can be rearranged based on their ranks
   Eg - Education(Based on their Salary) => (Phd-1, MS-2 , B.Com-3)

   • Label Encoding
   • Target Guided Ordinal Encoding

Nominal Encoding

1. One Hot Encoding-

   Germany	France	Spain
   o	1	0
   1	0	0
   0	0	1

   In The above example we have assigned one dummy variable to each Country Name and then after
   One of the new Variables can be dropped.

   Disadvantage- If we have many no of distinct categories then apply One Hot Encoding will
   increase the dimension of of data and hence time to compute

   ode = OrdinalEncoder(categories=[['Child','Teen','Adult','Old'],[1,2,3],[1,3,4,5,6,7],['Low','Medium','High'],['Alone','Small','Medium','Large']])
   
   df_train[['Age','Pclass','Ticket','Fare','Family']] = ode.fit_transform(df_train[['Age','Pclass','Ticket','Fare','Family']])
   

   ode = OrdinalEncoder(categories=[['Child','Teen','Adult','Old'],[1,2,3],[1,3,4,5,6,7],['Low','Medium','High'],['Alone','Small','Medium','Large']])
   
   df_train[['Age','Pclass','Ticket','Fare','Family']] = ode.fit_transform(df_train[['Age','Pclass','Ticket','Fare','Family']])
   

2. One Hot Encoding with Multiple Variable-
   We can tackle this by using One Hot Encoding to the top 10 categories which occur most of the time and rest of the categories as one whole category

3. Mean Encoding-
   We use this kind of Encoding where we have many categories like we have Pincodes of many citites then we can find the mean of Each Pincode that how many Times that Pincode returns Zero to the total no. of times that pincode occur.

Ordinal Encoding

1. Label Encoding-

   Education
   BE	1
   MAS	2
   Phd	3

   Here we have assigned a value according to their rank

   df_test = pd.get_dummies(df_test, columns=['Sex','Embarked','Title'],drop_first=True)
   

   df_test = pd.get_dummies(df_test, columns=['Sex','Embarked','Title'],drop_first=True)
   

2. Target Guided Ordinal Encoding-
   In This we calculate the mean of each category that how many times the output regarding that came 1 and then rank each category according to the order of their mean

Column Transformer

• SimpleImputer = Fill all the null values with mean (used in Numerical features)

B. Numerical Encoding

1. Discritzation (Binning)
2. Binarization

1. Binning

1.1 Unsupervised Binning
    1.1.1 Equal Width Binning (Uniform Binning)
    1.1.2 Equal Frequency Binning (Quantile Binning)
    1.1.3 K Means Binning
1.2 Supervised Binning
    1.2.1 Decision Tree Binning
1.3 Custom Binning 

1.1 Unsupervised Binning
    1.1.1 Equal Width Binning (Uniform Binning)
    1.1.2 Equal Frequency Binning (Quantile Binning)
    1.1.3 K Means Binning
1.2 Supervised Binning
    1.2.1 Decision Tree Binning
1.3 Custom Binning 

1.1.1 Equal Width Binning

1.1.2 Equal Frequency Binning

1.1.3 K Means Binning

• In this we calculate the distance of each point from all centroid and move that point to that set from where its distance is minimum
• After that we move the centroid to the mean of their data points and then repat the above step

from sklearn.preprocessing import KBinsDiscretizer
kbin_age = KBinsDiscretizer(n_bins=10, encode= 'ordinal', strategy='quantile')
kbin_fare = KBinsDiscretizer(n_bins=10, encode= 'ordinal', strategy='quantile')

from sklearn.preprocessing import KBinsDiscretizer
kbin_age = KBinsDiscretizer(n_bins=10, encode= 'ordinal', strategy='quantile')
kbin_fare = KBinsDiscretizer(n_bins=10, encode= 'ordinal', strategy='quantile')